Proprioceptive Feedback Control Improves Peristaltic Turning in Confined Environments

Area of Science:

• Robotics
• Soft Robotics
• Control Systems

Background:

• Soft and flexible robots are ideal for confined spaces where traditional robots struggle.
• Undulatory and peristaltic robots navigate enclosed tunnels effectively but face challenges with sharp turns and T-junctions.

Purpose of the Study:

• To develop the first closed-loop, peristaltic turning algorithm for soft robots in confined spaces.
• To enable soft robots to navigate complex geometries like sharp turns and T-junctions autonomously.

Main Methods:

• Developed a closed-loop peristaltic turning algorithm that scales turning angle based on contact force feedback.
• Verified the controller using a dynamic model of a soft worm robot in pipe bends of varying radii, diameters, and geometries.
• Compared open-loop and closed-loop controllers using speed and cost of transport as metrics.

Main Results:

• The adaptive algorithm maintained high speed performance across all tested bends, with only a 1.6% average speed loss compared to optimal fixed strategies.
• The adaptive turning enabled traversal of small radius bends (0.5-0.6 m) where straight-line locomotion failed.
• Adaptive turning demonstrated the lowest cost of transport in most pipe bend geometries and autonomously transitioned between turn directions and spiral turns.

Conclusions:

• The developed adaptive turning algorithm significantly enhances the maneuverability of soft robots in confined environments.
• This controller equips worm robots with advanced turning capabilities for diverse unstructured and confined spaces.
• The adaptive algorithm represents a breakthrough for autonomous navigation in challenging subterranean or internal structures.